Editing Expert system (section)

== Applications ==
Hayes-Roth divides expert systems applications into 10 categories illustrated in the following table. The example applications were not in the original Hayes-Roth table, and some of them arose well afterward. Any application that is not footnoted is described in the Hayes-Roth book.<ref name="Hayes-Roth 1983"/> Also, while these categories provide an intuitive framework to describe the space of expert systems applications, they are not rigid categories, and in some cases an application may show traits of more than one category.

{|class="wikitable"
|-
! Category !! Problem addressed !! Examples
|-
| Interpretation || Inferring situation descriptions from sensor data || Hearsay (speech recognition), PROSPECTOR
|-
| Prediction || Inferring likely consequences of given situations || Preterm Birth Risk Assessment<ref>{{cite journal |last1=Woolery |first1=L.K. |last2=Grzymala-Busse |first2=J. |year=1994 |title=Machine learning for an expert system to predict preterm birth risk |journal=Journal of the American Medical Informatics Association |volume=1 |issue=6 |pages=439–446 |pmc=116227 |pmid=7850569 |doi=10.1136/jamia.1994.95153433}}</ref>
|-
| Diagnosis || Inferring system malfunctions from observables || [[CADUCEUS (expert system)|CADUCEUS]], [[MYCIN]], PUFF, Mistral,<ref name="Mistral">{{cite journal |last1=Salvaneschi |first1=Paolo |first2=Mauro |last2=Cadei |first3=Marco |last3=Lazzari |year=1996 |title=Applying AI to structural safety monitoring and evaluation |journal=IEEE Expert |volume=11 |issue=4 |pages=24–34 |url=http://www.computer.org/csdl/mags/ex/1996/04/x4024-abs.html|access-date=5 March 2014 |doi=10.1109/64.511774 |url-access=subscription}}</ref> Eydenet,<ref name="Eydenet"/> Kaleidos,<ref name="Kaleidos"/> GARVAN-ES1<ref name="GARVAN">{{cite journal |author1=K. Horn |author2=L. Lazarus |author3=P. Compton |author4=J.R. Quinlan |title=An expert system for the interpretation of thyroid assays in a clinical laboratory |journal=Aust Comput J |year=1985 |volume=17 |pages=7–11}}</ref><ref name="Buchanan">{{cite journal |author=Buchanan, B. |title=Expert systems: working systems and the research literature |journal=Expert Systems |year=1986 |volume=3 |issue=1 |pages=32–51 |doi= 10.1111/j.1468-0394.1986.tb00192.x}}</ref><ref name="Maintenance">{{cite journal |author1=P. Compton |author2=K. Horn |author3=R. Quinlan |author4=L. Lazarus |author5=K. Ho |title=Maintaining an Expert System |journal=Proceedings of the Fourth Australian Conference on the Applications of Expert Systems |year=1988}}</ref> 
|-
| Design || Configuring objects under constraints || [[Dendral]], [[Expert systems for mortgages|Mortgage Loan Advisor]], [[Xcon|R1]] (DEC VAX Configuration), SID (DEC [[VAX 9000]] [[central processing unit|CPU]])
|-
| [[Automated planning and scheduling|Planning]] || Designing actions || Mission Planning for Autonomous Underwater Vehicle<ref>{{cite journal |last=Kwak |first=S. H. |year=1990 |title=A mission planning expert system for an autonomous underwater vehicle |journal=Proceedings of the 1990 Symposium on Autonomous Underwater Vehicle Technology |pages=123–128|doi=10.1109/AUV.1990.110446 |s2cid=60476847}}</ref>
|-
| Monitoring || Comparing observations to plan vulnerabilities || REACTOR<ref>{{cite journal |last=Nelson |first=W. R. |title=REACTOR: An Expert System for Diagnosis and Treatment of Nuclear Reactors |url=https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=4daf79217bb76482943f07b5138104a45f85c076 |year=1982 |journal=AAAI |volume=82: Proceedings of the Second AAAI Conference on Artificial Intelligence |pages=296–301}}</ref>
|-
| Debugging || Providing incremental solutions for complex problems || SAINT, MATHLAB, MACSYMA
|-
| Repair || Executing a plan to administer a prescribed remedy || Toxic Spill Crisis Management
|-
| Instruction || Diagnosing, assessing, and correcting student behaviour || SMH.PAL,<ref name="smhpal"/> Intelligent Clinical Training,<ref>{{cite journal |last1=Haddawy |first1=P |first2=S. |last2=Suebnukarn |year=2010 |title=Intelligent Clinical Training Systems |journal=Methods Inf Med |volume=49 |issue=4 |pages=388–9 |citeseerx=10.1.1.172.60 |pmid=20686730|doi=10.1055/s-0038-1625342 |s2cid=11903941}}</ref> STEAMER<ref>{{cite journal |last1=Hollan |first1=J. |first2=E. |last2=Hutchins |first3=L. |last3=Weitzman |year=1984 |title=STEAMER: An interactive inspectable simulation-based training system |journal=AI Magazine}}</ref>
|-
| Control || Interpreting, predicting, repairing, and monitoring system behaviors || Real Time Process Control,<ref>{{cite journal |last=Stanley |first=G.M. |title=Experience Using Knowledge-Based Reasoning in Real Time Process Control |journal=Plenary Paper Presented at: International Federation of Automatic Control (IFAC) Symposium on Compute R Aided Design in Control Systems|date=July 15–17, 1991|url=http://www.gregstanleyandassociates.com/whitepapers/IFAC91objectPaper.pdf|access-date=3 December 2013}}</ref> Space Shuttle Mission Control,<ref>{{cite journal |last1=Rasmussen |first1=Arthur |first2=John F. |last2=Muratore |first3=Troy A. |last3=Heindel |title=The INCO Expert System Project: CLIPS in Shuttle mission control |journal=NTRS|date=February 1990|url=https://www.researchgate.net/publication/4702412|access-date=30 November 2013}}</ref> Smart Autoclave Cure of Composites<ref>{{Cite book |author1=Ciriscioli, P. R. |author2=G. S. Springer |title="Smart Autoclave Cure of Composites" |year=1990 |isbn=9781003209010}}</ref>
|}

Hearsay was an early attempt at solving [[Speech recognition|voice recognition]] through an expert systems approach. For the most part this category of expert systems was not all that successful. Hearsay and all interpretation systems are essentially pattern recognition systems—looking for patterns in noisy data. In the case of Hearsay recognizing phonemes in an audio stream. Other early examples were analyzing sonar data to detect Russian submarines. These kinds of systems proved much more amenable to a [[artificial neural network|neural network]] AI solution than a rule-based approach.

CADUCEUS and [[MYCIN]] were medical diagnosis systems. The user describes their symptoms to the computer as they would to a doctor and the computer returns a medical diagnosis.

Dendral was a tool to study hypothesis formation in the identification of organic molecules. The general problem it solved—designing a solution given a set of constraints—was one of the most successful areas for early expert systems applied to business domains such as salespeople configuring [[Digital Equipment Corporation]] (DEC) [[VAX]] computers and mortgage loan application development.

SMH.PAL is an expert system for the assessment of students with multiple disabilities.<ref name="smhpal">{{cite journal |last=Hofmeister |first=Alan |title=SMH.PAL: an expert system for identifying treatment procedures for students with severe disabilities. |journal=Exceptional Children |year=1994 |volume=61 |issue=2|url=http://www.freepatentsonline.com/article/Exceptional-Children/15824013.html|access-date=30 November 2013 |archive-url=https://web.archive.org/web/20131203033204/http://www.freepatentsonline.com/article/Exceptional-Children/15824013.html |archive-date=3 December 2013|url-status=dead}}</ref>

GARVAN-ES1 was a medical expert system, developed at the [[Garvan Institute of Medical Research]], that provided automated clinical diagnostic comments on endocrine reports from a pathology laboratory. It was one of the first medical expert systems to go into routine clinical use internationally<ref name="Buchanan"/> and the first expert system to be used for diagnosis daily in Australia.<ref name="Catlett">{{cite journal |author=Catlett, J. |title=Expert systems, the risks and rewards |journal=Hub Information Technology |year=1990 |volume=2 |issue=7 |pages=20–26}}</ref> The system was written in "C" and ran on a PDP-11 in 64K of memory. It had 661 rules that were compiled; not interpreted.

Mistral<ref name="Mistral"/> is an expert system to monitor dam safety, developed in the 1990s by Ismes (Italy). It gets data from an automatic monitoring system and performs a diagnosis of the state of the dam. Its first copy, installed in 1992 on the [[Ridracoli]] Dam (Italy), is still operational 24/7/365. It has been installed on several dams in Italy and abroad (e.g., [[Itaipu Dam]] in Brazil), and on landslide sites under the name of Eydenet,<ref name="Eydenet">{{cite journal |last1=Lazzari |first1=Marco |first2=Paolo |last2=Salvaneschi |title=Embedding a geographic information system in a decision support system for landslide hazard monitoring |journal=International Journal of Natural Hazards |year=1999 |volume=20 |issue=2–3 |pages=185–195|url=http://dinamico2.unibg.it/lazzari/doc/embedding-authors-copy.pdf|doi=10.1023/A:1008187024768 |bibcode=1999NatHa..20..185L |s2cid=1746570}}</ref> and on monuments under the name of Kaleidos.<ref name="Kaleidos">{{cite journal |last1=Lancini |first1=Stefano |first2=Marco |last2=Lazzari |first3=Alberto |last3=Masera |first4=Paolo |last4=Salvaneschi |title=Diagnosing Ancient Monuments with Expert Software |journal=Structural Engineering International |year=1997 |volume=7 |issue=4 |pages=288–291|url=http://dinamico2.unibg.it/lazzari/doc/structural-engineering-authors-copy.pdf|doi=10.2749/101686697780494392 |s2cid=8113173}}</ref> Mistral is a registered trade mark of [[Centro Elettrotecnico Sperimentale Italiano|CESI]].{{Missing information|section|which date ''"is still"'' in ''"Its first copy, installed in 1992 on the Dam (Italy), is still operational 24/7/365."'' refers to, all the sources cited seem to be from before 2000|date=January 2022}}